Accelerating Adoption Of The Modern Data Stack At 5X Data
Data Engineering Podcast
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Data Engineering Podcast - Part 2
Prophecy dot io is a low code data engineering platform for the cloud. It provides an easy to use visual interface to design and deploy data pipe lines on apache, spark and apache air flow. Lig: What are some of the categories of tools or vendors that you see as being ripe for consolidation? We haven't seen any big players in this space yet but i think those will be the next core parts of future platforms.
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Summary
The modern data stack is a constantly moving target which makes it difficult to adopt without prior experience. In order to accelerate the time to deliver useful insights at organizations of all sizes that are looking to take advantage of these new and evolving architectures Tarush Aggarwal founded 5X Data. In this episode he explains how he works with these companies to deploy the technology stack and pairs them with an experienced engineer who assists with the implementation and training to let them realize the benefits of this architecture. He also shares his thoughts on the current state of the ecosystem for modern data vendors and trends to watch as we move into the future.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. Now all the data users can use software engineering best practices – git, tests and continuous deployment with a simple to use visual designer. How does it work? – You visually design the pipelines, and Prophecy generates clean Spark code with tests on git; then you visually schedule these pipelines on Airflow. You can observe your pipelines with built in metadata search and column level lineage. Finally, if you have existing workflows in AbInitio, Informatica or other ETL formats that you want to move to the cloud, you can import them automatically into Prophecy making them run productively on Spark. Create your free account today at dataengineeringpodcast.com/prophecy.
- So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at dataengineeringpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.
- Your host is Tobias Macey and today I’m interviewing Tarush Agarwal about how he and his team are helping organizations streamline adoption of the modern data stack
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what you are doing at 5x and the story behind it?
- How has your focus and operating model shifted since we spoke a year ago?
- What are the biggest shifts in the market for data management that you have seen in that time?
- What are the main challenges that your customers are facing when they start working with you?
- What are the components that you are relying on to build repeatable data platforms for your customers?
- What are the sharp edges that you have had to smooth out to scale your implementation of those systems?
- What do you see as the white spaces that still exist in the offerings available for the "modern data stack"?
- With the rapid introduction of so many new products in the data ecosystem, what are the categories that you see as being a long-term necessity?
- What are the areas that you predict will merge and consolidate over the next 3 – 5 years?
- What are the most interesting, innovative, or unexpected types of problems that you and your collaborators have had the opportunity to work on?
- What are the most interesting, unexpected, or challenging lessons that you have learned while building the 5x organization?
- When is 5x the wrong choice?
- What do you have planned for the future of 5x?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Links
- 5X Data
- Snowflake
- dbt
- Fivetran
- Looker
- Matt Turck State of Data
- Mixpanel
- Amplitude
- Heap
- Bigquery
- Narrator
- Marquez
- Atlan
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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